Lidar waveform generation system

ABSTRACT

A light detection and ranging (LIDAR) system includes a LIDAR measurement unit, a reference measurement unit, and a phase cancellation unit. The LIDAR measurement unit estimates a time for which a laser beam travels. The reference measurement unit determines a phase of a laser source. The phase cancellation unit identifies phase noise and cancels the phase noise from the laser beam, at least partially based on the phase of the laser source and the time for which the laser beam travels. The denoised signal is used to determine the range between a laser source and a target.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Application No.63/074,832 filed Sep. 4, 2020, which is hereby incorporated byreference.

TECHNICAL FIELD

This disclosure relates generally to light detection and ranging(LIDAR).

BACKGROUND INFORMATION

Frequency modulated continuous wave (FMCW) LIDAR directly measures rangeand velocity of an object by directing a frequency modulated, collimatedlight beam at a target. Both range and velocity information of thetarget can be derived from FMCW LIDAR signals. Designs and techniques toincrease the accuracy of LIDAR signals are desirable.

The automobile industry is currently developing autonomous features forcontrolling vehicles under certain circumstances. According to SAEInternational standard J3016, there are 6 levels of autonomy rangingfrom Level 0 (no autonomy) up to Level 5 (vehicle capable of operationwithout operator input in all conditions). A vehicle with autonomousfeatures utilizes sensors to sense the environment that the vehiclenavigates through. Acquiring and processing data from the sensors allowsthe vehicle to navigate through its environment. Autonomous vehicles mayinclude one or more FMCW LIDAR devices for sensing its environment.

BRIEF SUMMARY OF THE INVENTION

Implementations of the disclosure include a light detection and ranging(LIDAR) system including a LIDAR measurement unit, a referencemeasurement unit, and a phase cancellation unit. The LIDAR measurementunit is configured to estimate a time for which a laser beam travelsbetween a laser source and a target. The reference measurement unit isconfigured to determine a phase of the laser source. The phasecancellation unit is configured to cancel phase noise from a signalrepresenting the laser beam, at least partially based on the phase ofthe laser source and the time for which the laser beam travels.

In an implementation, the LIDAR system further includes a free-spaceinterferometer and a fixed-length interferometer. The signalrepresenting the laser beam is a first beat signal received from thefree-space interferometer. The phase of the laser source is calculatedfrom a second beat signal received from the fixed-length interferometer.The laser source concurrently feeds the free-space interferometer andthe fixed-length interferometer.

In an implementation, the free-space interferometer combines a firstlocal oscillator signal with a target-reflected signal to generate thefirst beat signal, and the fixed-length interferometer combines a secondlocal oscillator signal with a fixed-length signal that is delayed by afixed-length optical delay line to generate the second beat signal.

In an implementation, the phase cancellation unit generates a delayedphase of the laser source with a delay operation that is configured todelay the phase of the laser source by the time for which the laser beamtravels estimated by the LIDAR measurement unit.

In an implementation, the phase cancellation unit subtracts the delayedphase of the laser source from the phase of the laser source to generatea delta phase of the laser source. The delta phase of the laser sourcerepresents the phase noise within the signal representing the laserbeam.

In an implementation, the phase cancellation unit multiplies a complexconjugate of the delta phase with the signal representing the laser beamto cancel the phase noise.

In an implementation, the LIDAR system further includes a rangecalculation unit configured to calculate a range between the lasersource and the target from a denoised signal that is the signalrepresenting the laser beam after cancellation of the phase noise.

In an implementation, the range calculation unit determines a frequencyof the denoised signal, and the frequency of the denoised signal isdetermined based on peak amplitudes of a frequency representation of thedenoised signal.

In an implementation, the LIDAR system is a frequency modulatedcontinuous wave (FMCW) LIDAR system.

In an implementation, the reference measurement unit determines thephase of the laser source at least partially based on an in-phase signaland a quadrature signal from a fixed-length interferometer.

In an implementation, to determine the phase of the laser source, thereference measurement unit is configured to apply an arctangentoperation to the quadrature signal divided by the in-phase signal and isconfigured to apply an integration operation to output from thearctangent operation.

In an implementation, to estimate the time for which the laser beamtravels, the LIDAR measurement unit is configured to determine afrequency of a beat signal from a free-space interferometer, and thefrequency of the beat signal is determined based on at least one peakamplitude of a frequency representation of the beat signal.

An implementation of the disclosure includes an autonomous vehiclecontrol system including a light detection and ranging (LIDAR) system.The LIDAR system includes a LIDAR measurement unit, a referencemeasurement unit, and a phase cancellation unit. The LIDAR measurementunit is configured to estimate a time for which a laser beam travelsbetween a laser source and a target. The reference measurement unit isconfigured to determine a phase of the laser source. The phasecancellation unit is configured to cancel phase noise from a signalrepresenting the laser beam, at least partially based on the phase ofthe laser source and the time for which the laser beam travels. Thecontrol system for the autonomous vehicle includes one or moreprocessors to control the autonomous vehicle control system in responseto signals output by the phase cancellation unit.

In an implementation, the LIDAR system further includes a free-spaceinterferometer a fixed-length interferometer. The signal representingthe laser beam is a first beat signal received from the free-spaceinterferometer. The phase of the laser source is calculated from asecond beat signal received from the fixed-length interferometer. Thelaser source concurrently feeds the free-space interferometer and thefixed-length interferometer.

In an implementation, the phase cancellation unit is configured togenerate a delayed phase of the laser source with a delay operation thatis configured to delay the phase of the laser source by the time forwhich the laser beam travels estimated by the LIDAR measurement unit.

In an implementation, the phase cancellation unit subtracts the delayedphase of the laser source from the phase of the laser source to generatea delta phase of the laser source. The delta phase of the laser sourcerepresents the phase noise within the signal representing the laserbeam. The phase cancellation unit is configured to multiply a complexconjugate of the delta phase with the signal representing the laser beamto cancel the phase noise.

An implementation of the disclosure includes an autonomous vehiclesystem for an autonomous vehicle including a light detection and ranging(LIDAR) system. The LIDAR system includes a LIDAR measurement unit, areference measurement unit, and a phase cancellation unit. The LIDARmeasurement unit is configured to estimate a time for which a laser beamtravels between a laser source and a target. The reference measurementunit is configured to determine a phase of the laser source. The phasecancellation unit is configured to cancel phase noise from a signalrepresenting the laser beam, at least partially based on the phase ofthe laser source and the time for which the laser beam travels. Theautonomous vehicle includes one or more processors to control theautonomous vehicle in response to signals output by the phasecancellation unit.

In an implementation, the LIDAR system further includes a free-spaceinterferometer and a fixed-length interferometer. The signalrepresenting the laser beam is a first beat signal received from thefree-space interferometer. The phase of the laser source is calculatedfrom a second beat signal received from the fixed-length interferometer.The laser source concurrently feeds the free-space interferometer andthe fixed-length interferometer.

In an implementation, the phase cancellation unit is configured togenerate a delayed phase of the laser source with a delay operation thatis configured to delay the phase of the laser source by the time forwhich the laser beam travels estimated by the LIDAR measurement unit.

In an implementation, the phase cancellation unit is configured tosubtract the delayed phase of the laser source from the phase of thelaser source to generate a delta phase of the laser source. The deltaphase of the laser source represents the phase noise within the signalrepresenting the laser beam. The phase cancellation unit is configuredto multiply a complex conjugate of the delta phase with the signalrepresenting the laser beam to cancel the phase noise.

Implementations of the disclosure include a light detection and ranging(LIDAR) system including a laser waveform function, a set of parameters,and a calibration unit. The laser waveform function defines a laserwaveform. The set of parameters at least partially define the laserwaveform. The calibration unit is configured to estimate a partialderivative of a frequency response with respect to each parameter in theset of parameters. The frequency response is measured from an output ofa laser driven by the laser waveform. The calibration unit is configuredto update the set of parameters to cause the frequency response of thelaser to satisfy conditions defined by the laser waveform function.

In an implementation, the LIDAR system further includes a fixed-lengthinterferometer. The calibration unit is configured to receive anin-phase signal and a quadrature signal from the fixed-lengthinterferometer. The calibration unit is configured to determine afrequency response of the laser based on the in-phase signal and thequadrature signal.

In an implementation, the calibration unit is configured to iterativelyconstruct the laser waveform based the laser waveform function and theset of parameters. The set of parameters includes an initial version ofthe set of parameters that is superseded by one or more updated versionsof the set of parameters.

In an implementation, the calibration unit is configured to iterativelyevaluate the frequency response of the laser with updated versions ofthe set of parameters.

In an implementation, to iteratively evaluate the frequency response ofthe laser, the calibration unit is configured to load the laser waveforminto a digital to analog converter, wait for the laser to settle,measure an output from an interferometer, and calculate the frequencyresponse from the output from the interferometer.

In an implementation, the calibration unit is configured to estimate agradient of the laser waveform function.

In an implementation, to estimate the gradient of the laser waveformfunction, the calibration unit is configured to calculate a perturbedversion of the laser waveform, load the perturbed version of the laserwaveform into a digital to analog converter, measure the output from thelaser, and evaluate a perturbed version of the laser waveform function.

In an implementation, the perturbed version of the laser waveformincludes a difference between a first parameter in the set of parametersand a second parameter in the set of parameters.

In an implementation, the calibration unit is configured to update theset of parameters based on the partial derivative of the frequencyresponse with respect to each parameter in the set of parameters.

In an implementation, the calibration unit is configured to update theset of parameters to compensate for distortion characteristics of thelaser.

An implementation of the disclosure includes an autonomous vehiclecontrol system including a light detection and ranging (LIDAR) system.The LIDAR system includes a laser waveform function to define a laserwaveform, a set of parameters that at least partially define the laserwaveform, and a calibration unit configured to estimate a partialderivative of a frequency response with respect to each parameter in theset of parameters. The frequency response is measured from an output ofa laser driven by the laser waveform. The calibration unit is configuredto update the set of parameters to cause the frequency response of thelaser to satisfy conditions defined by the laser waveform function. Theautonomous vehicle control system includes one or more processors tocontrol the autonomous vehicle control system in response to the laserwaveform at least partially defined by the calibration unit.

In an implementation, the autonomous vehicle control system furtherincludes a fixed-length interferometer. The calibration unit isconfigured to receive an in-phase signal and a quadrature signal fromthe fixed-length interferometer. The calibration unit is configured todetermine a frequency response of the laser based on the in-phase signaland the quadrature signal.

In an implementation, the calibration unit is configured to iterativelyconstruct the laser waveform based the laser waveform function and theset of parameters. The set of parameters includes an initial version ofthe set of parameters that is superseded by one or more updated versionsof the set of parameters.

In an implementation, the calibration unit is configured to iterativelyevaluate the frequency response of the laser with updated versions ofthe set of parameters.

In an implementation, to iteratively evaluate the frequency response ofthe laser, the calibration unit is configured to load the laser waveforminto a digital to analog converter, wait for the laser to settle,measure an output from an interferometer, and calculate the frequencyresponse from the output from the interferometer.

In an implementation, the calibration unit is configured to estimate agradient of the laser waveform function.

In an implementation, to estimate the gradient of the laser waveformfunction, the calibration unit is configured to calculate a perturbedversion of the laser waveform, load the perturbed version of the laserwaveform into a digital to analog converter, measure the output from thelaser, and evaluate a perturbed version of the laser waveform function.

In an implementation, an autonomous vehicle includes a light detectionand ranging (LIDAR) system. The LIDAR system includes a laser waveformfunction to define a laser waveform, a set of parameters that at leastpartially define the laser waveform, and a calibration unit configuredto estimate a partial derivative of a frequency response with respect toeach parameter in the set of parameters. The frequency response ismeasured from an output of a laser driven by the laser waveform. Thecalibration unit is configured to update the set of parameters to causethe frequency response of the laser to satisfy conditions defined by thelaser waveform function. The autonomous vehicle includes one or moreprocessors to control the autonomous vehicle in response to the laserwaveform at least partially defined by the calibration unit.

In an implementation, the calibration unit is configured to iterativelyevaluate the frequency response of the laser with updated versions ofthe set of parameters.

In an implementation, the calibration unit is configured to estimate agradient of the laser waveform function.

Implementations of the disclosure include a light detection and ranging(LIDAR) system including a reference measurement unit and a LIDARmeasurement unit. The reference measurement unit is configured todetermine a phase of a reference beat signal from a fixed-lengthinterferometer driven by a laser source. The LIDAR measurement unit isconfigured to range multiple targets at least partially based on firstfrequency spectrum peaks from an upward frequency chirp of the lasersource paired to second frequency spectrum peaks from a downwardfrequency chirp of the laser source. The LIDAR measurement unit isconfigured to range the multiple targets at least partially based on thephase of the reference beat signal.

In an implementation, the LIDAR measurement unit is configured toestimate a travel time of a free-space laser signal to the multipletargets using sequentially paired peaks between the first frequencyspectrum peaks and the second frequency spectrum peaks. A first peakpair includes a first of the first frequency spectrum peaks and a firstof the second frequency spectrum peaks.

In an implementation, the LIDAR measurement unit is configured toiteratively range the multiple targets. Each of the multiple targets isassociated with a travel time estimate determined from a peak pair ofone of the first frequency spectrum peaks and one of the secondfrequency spectrum peaks.

In an implementation, the LIDAR measurement unit is configured to delaythe phase of the reference beat signal by a duration that is equal to atravel time estimate to identify phase noise of the laser source.

In an implementation, the LIDAR measurement unit is configured tomultiply a complex conjugate of the phase noise with a free-space beatsignal to cancel the phase noise in the free-space beat signal togenerate a denoised free-space beat signal.

In an implementation, the LIDAR measurement unit is configured to cancelphase noise that occurs during the upward frequency chirp. The referencemeasurement unit is configured to cancel phase noise that occurs duringthe downward frequency chirp.

In an implementation, the first frequency spectrum peaks are generatedfrom a first beat signal from a free-space interferometer. The secondfrequency spectrum peaks are generated from a second beat signal fromthe free-space interferometer.

In an implementation, the free-space interferometer combines a firstlocal oscillator signal with a first target-reflected signal to generatethe first beat signal from the upward frequency chirp. The free-spaceinterferometer combines a second local oscillator signal with a secondtarget-reflected signal to generate the second beat signal from thedownward frequency chirp.

In an implementation, the LIDAR system is a frequency modulatedcontinuous wave (FMCW) LIDAR system.

In an implementation, the reference measurement unit determines thephase of the reference beat signal at least partially based on anin-phase signal and a quadrature signal from the fixed-lengthinterferometer.

In an implementation, to determine the phase of the reference beatsignal, the reference measurement unit is configured to apply anarctangent operation to the quadrature signal divided by the in-phasesignal and is configured to apply an integration operation to outputfrom the arctangent operation.

In an implementation, the reference beat signal is a first referencebeat signal. The phase of the first reference beat signal is generatedfrom the upward frequency chirp. The reference measurement unit isconfigured to estimate a phase of a second reference beat signal that isgenerated from the downward frequency chirp.

An implementation of the disclosure includes an autonomous vehiclecontrol system including a light detection and ranging (LIDAR) system.The LIDAR system includes a LIDAR measurement unit and a referencemeasurement unit. The reference measurement unit is configured todetermine a phase of a reference beat signal from a fixed-lengthinterferometer driven by a laser source. The LIDAR measurement unit isconfigured to range multiple targets at least partially based on firstfrequency spectrum peaks from an upward frequency chirp of the lasersource paired to second frequency spectrum peaks from a downwardfrequency chirp of the laser source. The LIDAR measurement unit isconfigured to range the multiple targets at least partially based on thephase of the reference beat signal. The autonomous vehicle controlsystem includes one or more processors to control the autonomous vehiclecontrol system in response to signals output by at least one of theLIDAR measurement unit and the reference measurement unit.

In an implementation, the LIDAR measurement unit is configured toestimate a travel time of a free-space laser signal to the multipletargets using sequentially paired peaks between the first frequencyspectrum peaks and the second frequency spectrum peaks. A first peakpair includes a first of the first frequency spectrum peaks and a firstof the second frequency spectrum peaks.

In an implementation, the LIDAR measurement unit is configured toiteratively range the multiple targets. Each of the multiple targets isassociated with a travel time estimate determined from a peak pair ofone of the first frequency spectrum peaks and one of the secondfrequency spectrum peaks.

In an implementation, the LIDAR measurement unit is configured to delaythe phase of the reference beat signal by a duration that is equal to atravel time estimate to identify phase noise of the laser source. TheLIDAR measurement unit is configured to multiply a complex conjugate ofthe phase noise with a free-space beat signal to cancel the phase noisein the free-space beat signal to generate a denoised free-space beatsignal.

An implementation of the disclosure includes an autonomous vehicleincluding a light detection and ranging (LIDAR) system. The LIDAR systemincludes a LIDAR measurement unit and a reference measurement unit. Thereference measurement unit is configured to determine a phase of areference beat signal from a fixed-length interferometer driven by alaser source. The LIDAR measurement unit is configured to range multipletargets at least partially based on first frequency spectrum peaks froman upward frequency chirp of the laser source paired to second frequencyspectrum peaks from a downward frequency chirp of the laser source. TheLIDAR measurement unit is configured to range the multiple targets atleast partially based on the phase of the reference beat signal. Theautonomous vehicle includes one or more processors to control theautonomous vehicle in response to signals output by at least one of theLIDAR measurement unit or the reference measurement unit.

In an implementation, the LIDAR measurement unit is configured toestimate a travel time of a free-space laser signal to the multipletargets using sequentially paired peaks between the first frequencyspectrum peaks and the second frequency spectrum peaks. A first peakpair includes a first of the first frequency spectrum peaks and a firstof the second frequency spectrum peaks.

In an implementation, the LIDAR measurement unit is configured toiteratively range the multiple targets. Each of the multiple targets isassociated with a travel time estimate determined from a peak pair ofone of the first frequency spectrum peaks and one of the secondfrequency spectrum peaks.

In an implementation, the LIDAR measurement unit is configured to delaythe phase of the reference beat signal by a duration that is equal to atravel time estimate to identify phase noise of the laser source. TheLIDAR measurement unit is configured to multiply a complex conjugate ofthe phase noise with a free-space beat signal to cancel the phase noisein the free-space beat signal to generate a denoised free-space beatsignal.

BRIEF DESCRIPTION OF THE DRAWINGS

Non-limiting and non-exhaustive implementations of the invention aredescribed with reference to the following figures, wherein likereference numerals refer to like parts throughout the various viewsunless otherwise specified.

FIG. 1 illustrates an optical measurement apparatus that supports phaseestimation, active phase cancellation, predistortion waveformgeneration, and peak pairing in a LIDAR system, in accordance withimplementations of the disclosure.

FIG. 2 illustrates a LIDAR system that may incorporate phase estimation,active phase cancellation, predistortion waveform generation, and peakpairing, in accordance with implementations of the disclosure.

FIGS. 3A and 3B illustrate example phase noise cancellation systems, inaccordance with implementations of the disclosure.

FIGS. 4A and 4B illustrate examples of predistortion waveformgenerators, in accordance with implementations of the disclosure.

FIGS. 5A and 5B illustrate examples of multi-target identificationsystems for an FMCW LIDAR system, in accordance with implementations ofthe disclosure.

FIGS. 6A and 6B illustrate an example of an operation cycle of a LIDARsystem that incorporates phase estimation, active phase cancellation,predistortion waveform generation, and peak pairing, according tovarious implementations of the disclosure.

FIG. 7A illustrates an autonomous vehicle including an array of examplesensors, in accordance with implementations of the disclosure.

FIG. 7B illustrates a top view of an autonomous vehicle including anarray of example sensors, in accordance with implementations of thedisclosure.

FIG. 7C illustrates an example vehicle control system including sensors,a drivetrain, and a control system, in accordance with implementationsof the disclosure.

DETAILED DESCRIPTION

Implementations of phase estimation, active phase cancellation,predistortion waveform generation, and peak pairing for light detectionand ranging (LIDAR) systems are described herein. In the followingdescription, numerous specific details are set forth to provide athorough understanding of the implementations. One skilled in therelevant art will recognize, however, that the techniques describedherein can be practiced without one or more of the specific details, orwith other methods, components, or materials. In other instances,well-known structures, materials, or operations are not shown ordescribed in detail to avoid obscuring certain aspects.

Reference throughout this specification to “one implementation” or “animplementation” means that a particular feature, structure, orcharacteristic described in connection with the implementation isincluded in at least one implementation of the present invention. Thus,the appearances of the phrases “in one implementation” or “in animplementation” in various places throughout this specification are notnecessarily all referring to the same implementation. Furthermore, theparticular features, structures, or characteristics may be combined inany suitable manner in one or more implementations.

Throughout this specification, several terms of art are used. Theseterms are to take on their ordinary meaning in the art from which theycome, unless specifically defined herein or the context of their usewould clearly suggest otherwise. For the purposes of this disclosure,the term “autonomous vehicle” includes vehicles with autonomous featuresat any level of autonomy of the SAE International standard J3016.

In aspects of this disclosure, visible light may be defined as having awavelength range of approximately 380 nm-700 nm. Non-visible light maybe defined as light having wavelengths that are outside the visiblelight range, such as ultraviolet light and infrared light. Infraredlight having a wavelength range of approximately 700 nm-1 mm includesnear-infrared light. In aspects of this disclosure, near-infrared lightmay be defined as having a wavelength range of approximately 700 nm-1.6μm.

In aspects of this disclosure, the term “transparent” may be defined ashaving greater than 90% transmission of light. In some implementations,the term “transparent” may be defined as a material having greater than90% transmission of visible light.

A coherent LIDAR system directly measures range and velocity of anobject by directing a modulated, collimated light beam at the object.The light that is reflected from the object is combined with a tappedversion of the beam. The frequency of the resulting beat tone isproportional to the distance of the object from the LIDAR system, oncecorrected for the doppler shift, which may use a second measurement. Thetwo measurements, which may or may not be performed at the same time,provide both range and velocity information. In this application,frequency modulated continuous wave (FMCW) LIDAR is described as oneexample of a coherent LIDAR. However, the implementations and examplesdescribed in this application can be applied to any type of coherentLIDAR.

In some implementations, FMCW LIDAR, which is a type of a coherentLIDAR, can be used. In particular, FMCW LIDAR modulates a frequency of alight beam from a laser source. FMCW LIDAR can take advantage ofintegrated photonics for improved manufacturability and performance.Integrated photonic systems may manipulate single optical modes usingmicron-scale waveguiding devices.

Integrated FMCW LIDAR systems rely on one or more laser sources, whichprovide optical power to the system. The optical field produced by suchlasers typically exhibits deterministic and stochastic phasefluctuations which can result in broadening of the returning FMCW beatsignal and thus compromise the system's performance.

FMCW LIDAR systems emit light which can reflect off of more than oneobject in a scene before returning to the unit. These multiple returnsresult in a beat signal with a spectrum having multiple peaks. In orderto determine the distance and velocity of each of these returns, twomeasurements of the beat signal can be performed. In order to correctlydetermine the multiple distances and velocities, the peaks in the twospectra are correctly paired.

FMCW LIDAR systems use a linear frequency chirp in order to achieve goodperformance. This linear frequency chirp can be achieved by driving thelaser with a laser drive waveform. To compensate for distortioncharacteristics of a laser, the laser drive waveform may be defined tocompensate for the characteristics of the transmitting laser. As thesystem ages, the linearity of the frequency chirp may degrade. Someimplementations of the disclosure provide for in situ recalibration ofthe laser drive waveform to support system degradation or changes.

A system is described for directly measuring the frequency excursion ofa laser in an integrated FMCW LIDAR system. Measured frequency excursioncan be integrated to determine the phase optical signal produced by thelaser.

The system comprises a short (fixed-length) integrated interferometerthat is connected or coupled in parallel to a main free-spaceinterferometer that at least partially defines the FMCW LIDAR system. Asingle laser source feeds both interferometers.

Using an initial estimate of target range from the main free-spaceinterferometer, undesired optical phase fluctuations (phase noise) inthe beat signal can be estimated and subtracted from the beat signal,thus improving the measurement capabilities of the main free-spaceinterferometer.

The estimated beat signal phase can additionally be used to correctlypair spectral peaks when multiple returns (multiple targets) arepresent.

The measured frequency excursion can equally be used for in-situgeneration and calibration of predistortion waveforms to improve thelinearity of the laser frequency chirp.

The systems and methods for identifying and canceling optical phasefluctuations (“phase noise”), for identifying multiple targets, and forgenerating predistortion waveforms described in this disclosure may(collectively or individually) be used to support autonomous operationof a vehicle. These and other implementations are described in moredetail in connection with FIGS. 1-7C.

FIG. 1 illustrates an optical phase measurement apparatus 100 thatsupports phase estimation, active phase cancellation, predistortionwaveform generation, and peak pairing in an FMCW LIDAR system, inaccordance with implementations of the disclosure. Optical phasemeasurement apparatus 100 includes a laser source 101, a splitter 102, afree-space interferometer 103, and a fixed-length interferometer 104 tosupport phase estimation, phase noise cancellation, predistortionwaveform generation, and peak pairing in a LIDAR system.

Light emitted by laser source 101 enters splitter 102 which splits theoptical power of laser source 101 into two separate optical channels.The split ratio achieved by splitter 102 may be equal (50:50) or someother ratio (e.g., 80:20). In practice, the majority of the opticalpower is split and routed to free-space interferometer 103, while theremaining small portion of the optical power is routed to fixed-lengthinterferometer 104.

Fixed-length interferometer 104 is configured to measure or approximatethe instantaneous laser frequency of laser source 101. Fixed-lengthinterferometer 104 may include a splitter 105, a fixed-length opticaldelay line 106, an optical hybrid 107, a balanced photodiode pair 108,and a balanced photodiode pair 109. Fixed-length interferometer 104 mayincorporate a short optical delay in fixed-length optical delay line106, e.g., in the range of 10 cm-30 cm. Fixed-length interferometer 104may therefore be referred to as a short fixed-length interferometer oras a reference interferometer.

Light entering fixed-length interferometer 104 passes through splitter105, which can have an equal or unequal split ratio. The top output ofsplitter 105 is connected to fixed-length optical delay line 106.Fixed-length optical delay line 106 delays the optical signal by a shortamount of time compared to light leaving the bottom output of splitter105. These two optical paths (i.e., the top optical path and the bottomoptical path) are connected to optical hybrid 107, which may beimplemented as a 2×4 optical hybrid. Optical hybrid 107 mixes the bottomsignal with the top signal that is delayed through fixed-length opticaldelay line 106. Because laser source 101 may be driven to output atime-based linearly changing frequency (i.e., chirped), the frequency ofthe top signal that arrives at optical hybrid 107 is slightly different(e.g., faster or slower) than the frequency of the bottom signal thatarrives at optical hybrid 107. When signals of different frequencies aremixed or combined, they produce a beat tone or beat signal that has abeat frequency that is equal to the difference of the two frequencies.

The beat signals from optical hybrid 107 are measured and converted toelectrical signals using balanced photodiode pair 108 and balancedphotodiode pair 109. Balanced photodiode pair 108 produces an electricalsignal that corresponds to an in-phase signal I_(ref) while balancedphotodiode pair 109 produces an electrical signal that corresponds to aquadrature signal Q_(ref). For a sufficiently short (e.g., 20 cm)implementation of fixed-length optical delay line 106, the phase ofin-phase signal I_(ref) and quadrature signal Q_(ref) measurements isproportional to an instantaneous frequency of laser source 101. Theinstantaneous frequency of laser source 101 can be integrated in orderto calculate the instantaneous phase of laser source 101. Theinstantaneous phase of laser source 101 may be used to isolate phasenoise of laser source 101, which may be defined by deterministic andstochastic phase fluctuations.

Free-space interferometer 103 is configured to measure or estimate thedistance between laser source 101 and a target. Free-spaceinterferometer 103 may include a splitter 110, a variable-distanceoptical delay line 111, an optical hybrid 112, a balanced photodiodepair 113, and a balanced photodiode pair 114.

Light entering free-space interferometer 103 enters into splitter 110.Splitter 110 separates the “local oscillator” field (i.e., theillustrated bottom path, “bottom signal”, and/or “local oscillatorsignal”) from the “signal” field (i.e., the illustrated top path, “topsignal”, and/or “delayed signal”). The top signal power is coupled intofree space. This light propagates over different or variable distancesbefore striking a target and reflecting back towards the LIDAR unit(e.g., free-space interferometer 103). This light is received byfree-space interferometer 103, effectively forming variable-distanceoptical delay line 111. The delayed signal and local oscillator signalare mixed together by optical hybrid 112. The outputs of optical hybrid112 are converted to electrical signals using balanced photodiode pair113 and balanced photodiode pair 114. The resulting electrical signalscorrespond to the in-phase signal I_(FS) and quadrature signal Q_(FS),which are components of the FMCW LIDAR beat signal, respectively. Thephase fluctuations in this measured beat signal are time correlated tothose in the beat signal measured by fixed-length interferometer 104,since the interferometers are concurrently fed by laser source 101.

FIG. 2 illustrates an example of an FMCW LIDAR system 200, which may beconfigured to incorporate phase estimation, active phase cancellation,predistortion waveform generation, and peak pairing, in accordance withimplementations of the disclosure. FMCW LIDAR system 200 includes aLIDAR processing engine 201 and a focal plane array (FPA) system 202. Inother implementations, a different form of beam steering may be used.

LIDAR processing engine 201 includes a microcomputer 203 configured todrive a digital-to-analog converter (DAC) 204, which generates amodulation signal for a laser controller 205. Laser controller 205modulates the frequency of a Q-channel laser array 206. The opticalpower emitted by laser array 206 is split and routed to a switchablecoherent pixel array 208 and to a laser phase reference interferometer207 (which may include fixed-length interferometer 104 of FIG. 1). Lightentering switchable coherent pixel array 208 is controlled by an FPAdriver 209. Light emitted from different locations in switchablecoherent pixel array 208 is collimated at different angles by lens 210and is emitted into free space 211.

The light emitted into free space 211 reflects off of targets,propagates back through lens 210, and is coupled back into switchablecoherent pixel array 208. The received light is measured using anN-channel receiver 212, which may incorporate optical hybrid 107 and/oroptical hybrid 112 (shown in FIG. 1). The resulting electrical currentsare digitized using one or more M-channel analog-to-digital converters(ADC) 213, and those signals are processed by the microcomputer 203.

In parallel with the free-space measurements, the optical field passingthrough laser phase reference interferometer 207 is measured using theP-channel receiver 214, which produces electrical currents that areconverted to a digital signal using the R-channel analog-to-digitalconverter (ADC) 215. This resulting digital signal is processed by themicrocomputer 203 in order to estimate the phase fluctuation (phasenoise) of the laser. The estimated or determined phase noise cansubsequently be used to cancel the phase noise from (“denoise”) thefree-space range measurement signals.

FIGS. 3A and 3B illustrate example phase noise cancellation systems bywhich optical phase measurement apparatus 100 (shown in FIG. 1) and FMCWLIDAR system 200 (shown in FIG. 2) can be used to actively cancelunwanted phase fluctuations (“phase noise”) to support FMCW LIDAR rangeand velocity measurements, in accordance with implementations of thedisclosure.

FIG. 3A illustrates a phase noise cancellation system 300, in accordancewith implementations of the disclosure. Phase noise cancellation system300 may include a LIDAR measurement unit 301, a reference measurementunit 302, a phase cancellation unit 303, and a range calculation unit304. LIDAR measurement unit 301 may be configured to perform an FMCWrange measurement by estimating time for which light travels (i.e.,travel time) between a light source and a target. In this application,the time for which light travels between a light source (e.g., a lasersource) and a target (e.g., an object in an environment at which a LIDARsystem is located) is defined as a time-of-flight. Concurrently,reference measurement unit 302 may be configured to determine anestimation of the phase of the light (e.g., laser light) by using areference or fixed-length interferometer. The time-of-flight estimateTest from LIDAR measurement unit 301 and the phase of the lightφ_(ex)(t) from reference measurement unit 302 are provided to phasecancellation unit 303. Phase cancellation unit 303 uses time-of-flightestimate Test and phase of the light φ_(ex)(t) to estimate and cancelphase noise from a signal representing the light (e.g., in-phase signalI_(FS) and/or quadrature signal Q_(FS)). Phase cancellation unit 303outputs a denoised signal V(t)_(dn) that is used by range calculationunit 304 to estimate a range to a target range. Range calculation unit304 may also be configured to calculate velocity of a target by, forexample, performing doppler shift calculations or measurements.

FIG. 3B illustrates an example of a phase noise cancellation system 340,in accordance with implementations of the disclosure. Phase noisecancellation system 340 is an example implementation of phase noisecancellation system 300. One or more components or operations withinphase noise cancellation system 340 may be implemented in a photonicintegrated circuit and/or FMCW LIDAR system.

LIDAR measurement unit 301 is configured to receive a signal 305 (e.g.,a voltage signal) and generate time-of-flight estimate Test. Signal 305is a signal that represents light that has traveled in free space to andfrom at least one target. Signal 305 may be a beat signal that is acombination of a local oscillator signal with a free-space light signal.Signal 305 may include in-phase signal IFS and/or quadrature signalQ_(FS) and may be received from free-space interferometer 103 (shown inFIG. 1).

LIDAR measurement unit 301 includes a frequency conversion block 306, afilter block 307, and a peak finding block 308. Frequency conversionblock 306 converts signal 305 into a frequency representation of signal305. LIDAR measurement unit 301 may use a Fourier transform (e.g., afast Fourier transform (FFT)) to perform this operation. Frequencyconversion block 306 may digitize signal 305 and may use the Fouriertransform to calculate the power spectral density (PSD) of signal 305.Filter block 307 filters the output of frequency conversion block 306 toimprove signal-to-noise ratio. Peak finding block 308 may identify thetallest peak in the filtered frequency spectrum of signal 305. Based onsystem parameters, this peak location can be converted to an estimate oftime-of-flight τ_(est) of the optical signal.

Concurrently with operation of LIDAR measurement unit 301, referencemeasurement unit 302 is configured to determine phase of the lightφ_(ex)(t). Reference measurement unit 302 may include a divide block311, an arctangent block 312, a phase unwrap block 313, and anintegration block 314. Reference measurement unit 302 receives in-phasesignal 310 and quadrature signal 309 as inputs from a fixed-lengthinterferometer. In-phase signal 310 and quadrature signal 309 arein-phase signal I_(ref) and quadrature signal Q_(ref) from fixed-lengthinterferometer 104, in one implementation. Divide block 311 includesdividing quadrature signal 309 by in-phase signal 310. Arctangent block312 performs the arctangent of the output of divide block 311 in orderto estimate the phase of the beat signal represented by at least one ofin-phase signal 310 and/or quadrature signal 309. Phase unwrap block 313applies phase unwrapping to the output of arctangent block 312. Theoutput of phase unwrap block 313 is integrated (with respect to time) atintegration block 314 in order to estimate the phase fluctuations of thesystem's laser with respect to time.

Phase cancellation unit 303 is configured to cancel phase noise fromsignal 305, at least partially based on phase of the light φ_(ex) andtime-of-flight τ_(est). Phase cancellation unit 303 includes a delayblock 315, a subtraction block 316, an exponent block 317, andmultiplication block 318. Delay block 315 generates delayed phaseφ_(ex)(t-τ_(est)), which is a time-delayed estimate of phase of thelight φ_(ex). The delay may be a digital delay, and the duration of thedelay is the duration of time-of-flight Test. By delaying phase of thelight φ_(ex)(t) by the duration of time-of-flight Test, phasecancellation unit 303 identifies a portion of phase of the lightφ_(ex)(t) that is associated with the light transmission that definessignal 305. Subtract block 316 subtracts phase of the light φ_(ex)(t)from delayed phase φ_(ex)(t-τ_(est)) to isolate a delta phaseΔφ_(ex)(τ_(est)) that defines phase fluctuations or phase noise at thetime the laser source transmitted signal 305. Exponent block 317constructs conjugate phasor from delta phase ←φ_(ex)(τ_(est)).Multiplication block 318 multiples the conjugate phasor by signal 305 tocreate denoised signal V(t)_(dn). Denoised signal V(t)_(dn) is signal305 with unwanted phase fluctuations canceled out or denoised. Denoisedsignal V(t)_(dn) is a resulting “clean” beat signal that may be passedto range calculation unit 304.

Range calculation unit 304 is configured to determine a range between alight source and a target, using denoised signal V(t)_(dn). Rangecalculation unit 304 includes a frequency conversion block 319, a filterblock 320, and a peak finding block 321. Frequency conversion block 319converts denoised signal V(t)_(dn) into a frequency representation ofdenoised signal V(t)_(dn). Range calculation unit 304 may use a Fouriertransform (e.g., a fast Fourier transform (FFT)) to perform thisoperation. Frequency conversion block 319 may digitize denoised signalV(t)_(dn) and may use the Fourier transform to calculate the powerspectral density (PSD) of denoised signal V(t)_(dn). Filter block 320filters the output of frequency conversion block 319 to improvesignal-to-noise ratio. Peak finding block 321 may identify one or morepeaks in the filtered frequency spectrum of denoised signal V(t)_(dn).This peak information is then used to estimate the position and velocityof a target. At block 322, phase noise cancellation system 340 endsoperations.

FIGS. 4A and 4B illustrate examples of predistortion waveformgenerators, in accordance with implementations of the disclosure. Thepredistortion waveform generators may use optical phase measurementapparatus 100 (shown in FIG. 1) and FMCW LIDAR system 200 (shown in FIG.2) to generate predistortion waveforms for driving a LIDAR system laser,or to refine (“calibrate”) existing predistortion waveforms.Predistortion waveform generation can be beneficial in a LIDAR systemdue to distortion characteristics of a laser. As an example, to linearlychange a laser's frequency up and/or down, a LIDAR system may beconfigured to drive a laser's frequency with a waveform, such as atriangle waveform. A triangle waveform linearly increases up in valueand linearly decreases down in value. However, distortioncharacteristics of a laser may cause the frequency response of the laserto generate an output that does not have a linearly increasing frequencyand a linearly decreasing frequency. Since FMCW LIDAR systems rely onfrequency modulation (e.g., chirping), such systems may benefit from apredistortion waveform that compensates for the distortioncharacteristics of the laser incorporated into a particular LIDARsystem. An in-place or in situ adjustment, refinement, or calibration ofpredistortion waveforms offers the advantage of compensating for eachlaser's minor unique operating characteristics.

FIG. 4A illustrates a predistortion waveform generator 400, inaccordance with implementations of the disclosure. Predistortionwaveform generator 400 includes a function definition block 401, aparameter set block 402, and a calibration unit 403. Prior to operationof a LIDAR system, function definition block 401 defines a meritfunction F(f). Merit function F(f) defines or quantifies the linearity(or shape) of the chirped laser frequency that is used to drive a laser.Similarly, prior to operation of a LIDAR system, parameter set block 402defines parameters p, which include a set of numbers that define theshape/behavior of the laser drive waveform. Merit function F(f) mayexplicitly be a function of the time-dependent frequency used to chirpthe LIDAR laser and may explicitly or implicitly be dependent onparameters p.

Calibration unit 403 is applied to a LIDAR system in order to findparameters p that minimizes merit function F(f). Calibration unit 403 isconfigured to generate a predistortion waveform to compensate fordistortion characteristics of a laser. Calibration unit 403 generatesthe predistortion waveform by applying partial derivatives to meritfunction F(f), with respect to each of parameters p in a set ofparameters p. Through iteratively identifying distortion characteristicsof a laser, calibration unit 403 redefines the set of parameters p,which are saved to define a laser drive waveform in future uses.

Predistortion waveform generator 400 ends operations at block 419.

FIG. 4B illustrates an example of a predistortion waveform generator430, in accordance with implementations of the disclosure. Predistortionwaveform generator 430 is an example implementation of predistortionwaveform generator 400 (shown in FIG. 4A).

Calibration unit 403 includes a number of operations or process blocksto support predistortion waveform generation. Calibration unit 403includes a construct waveform block 404, an evaluate function block 405,an estimate gradient block 411, and an update block 417. In constructwaveform block 404, calibration unit 403 constructs an initial drivewaveform V(t, p) from merit function F(f) and parameters p defined infunction definition block 401 and in parameter set block 402.

Next, the value of merit function F(f) is evaluated in evaluate functionblock 405. Evaluate function block 405 may include severalsub-operations. In block 406, the current version of drive waveform V(t,p) is loaded into a digital-to-analog converter (DAC) 406. At block 407,the laser that is driven by drive waveform V(t, p) is allowed to settleto steady-state operation. At block 408, in-phase signal I_(ref) andquadrature signal Q_(ref) are measured at the output of a shortreference interferometer (e.g., fixed-length interferometer 104, shownin FIG. 1). At block 409, in-phase signal I_(ref) and quadrature signalQ_(ref) are used to compute an estimate of a time-dependent laserfrequency f, for example, as described for reference measurement unit302 of FIG. 3B. At block 409, in-phase signal I_(ref) and quadraturesignal Q_(ref) are used to compute an estimate of time-dependent laserfrequency f by dividing quadrature signal Q_(ref) by in-phase signalI_(ref), performing the arctangent of the division result, unwrappingthe arctangent result, and dividing the quantity by the relative delayτ, of the fixed length interferometer. At block 410, a current value ofmerit function F(f) is computed using the time-dependent frequency fromblock 409.

After the current value of merit function F(f) is evaluated in evaluatefunction block 405, estimate gradient block 411 is configured toestimate a gradient of merit function F(f). Estimate gradient block 411is configured to determine the gradient by calculating the partialderivative of merit function F(f) with respect to each of parameters p.Block 412 includes perturbing the jth element of parameters p, computinga perturbed version of drive waveform V(p_(i)+Δp_(j)), and uploadingperturbed version of drive waveform V(p_(i)+Δp_(j)) into a DAC. Block413 includes evaluating a corresponding (perturbed) value of meritfunction F(p_(i)+Δp_(j)), for example, using sub-operations of evaluatefunction block 405. At block 414, a partial derivative δF/δp_(j) ofmerit function F(p_(i)+Δp_(j)) is estimated with respect to the jthelement of parameters p. Partial derivative δF/δp_(j) is approximatedusing a finite difference. At block 415, is it determined if additionalparameters p exist for perturbation. If more elements in parameters pexist, block 415 proceeds to block 416, where the value of j isincremented and estimate gradient block 411 is repeated. If each elementin parameters p has been evaluated, block 415 proceeds to block 417.

In update block 417, calibration unit 403 updates parameters p based onan evaluation merit function F(f) (from evaluate function block 405) andan estimate of the gradient of merit function F(f) (from estimategradient block 411). At block 418, calibration unit 403 performs aconvergence check. The convergence check is an evaluation of how closelythe frequency response of the laser match the defined merit functionF(f) when driven with parameters p. If merit function F(f) hasconverged, then a final version of parameters p and hence an optimizedrive signal V(t, p) is selected, and calibration unit 403 proceeds toblock 419 to end.

FIGS. 5A and 5B illustrate examples of multi-target identificationsystems that use reference phase measurements to identify multipletargets in an FMCW LIDAR system, in accordance with implementations ofthe disclosure. The multi-target identification systems apply phasemeasurement to pairings of multiple frequency spectrum return peaks inFMCW LIDAR beat signals.

FIG. 5A illustrates an example of multi-target identification system 500for using reference phase measurements to identify multiple targets inan FMCW LIDAR system. Multi-target identification system 500 includes aLIDAR measurement unit 551 and a reference measurement unit 552. LIDARmeasurement unit 551 includes some of the features of LIDAR measurementunit 301 (shown in FIGS. 3A and 3B), and reference measurement unit 552includes some of the features of reference measurement unit 302 (shownin FIGS. 3A and 3B), according to one implementation.

LIDAR measurement unit 551 is configured to range multiple targets.LIDAR measurement unit 551 is configured to range multiple targets byidentifying a first set of frequency spectrum peaks that have beengenerated from an upward frequency chirp of a laser source. LIDARmeasurement unit 551 is configured to range multiple targets byidentifying a second set of frequency spectrum peaks that have beengenerated from a downward frequency chirp of the laser source. LIDARmeasurement unit 551 is configured to pair peaks from the first set offrequency spectrum peaks with peaks from the second set of frequencyspectrum peaks, to confirm the existence of each of multiple targets andto estimate a time-of-flight to each of the multiple targets. LIDARmeasurement unit 551 is configured to use the phase φ_(ex)(t) of areference beat signal to denoise a free-space beat signal from which thefrequency spectrum peaks are derived.

Reference measurement unit 552 is configured to provide phasemeasurements of the laser source using a fixed-length interferometer.Reference measurement unit 552 is configured to determine a phaseφ_(ex)(t) of a reference beat signal from the fixed-lengthinterferometer and to provide phase φ_(ex)(t) of reference beat signalto LIDAR measurement unit 551 to enable LIDAR measurement unit 551 tocancel phase noise. Reference measurement unit 552 may calculate a firstphase from a first reference beat signal created by an upward frequencychirp. Reference measurement unit 552 may calculate a second phase froma second reference beat signal created by a downward frequency chirp.Reference measurement unit 552 is configured to provide the first phasefrom the first reference beat to LIDAR measurement unit 551 to enablephase noise cancellation from a free-space beat signal from an upwardfrequency chirp. Reference measurement unit 552 is configured to use thesecond phase from the second reference beat to cancel phase noise from afree-space beat signal from a downward frequency chirp.

Operations of multi-target identification system 500 end at block 553.

FIG. 5B illustrates an example of a multi-target identification system570 that uses reference phase measurements to identify multiple targetsin an FMCW LIDAR system, in accordance with implementations of thedisclosure. Multi-target identification system 570 is an exampleimplementation of multi-target identification system 500.

Initially, beat signals are generated from a laser source. At block 501a free-space beat signal is generated from a free-space interferometerusing an upwards frequency chirp (up-ramp). At block 502 a free-spacebeat signal is generated from the free-space interferometer using adownwards frequency chirp (down-ramp). Both free-space beat signals arecollected using the FMCW LIDAR system. These beat signals correspond torange and velocity measurements through free space. At block 503 thepower spectral density (PSD) of the up-ramp beat signal is calculated,and the locations (frequencies) of the tallest N peaks are located infrequency spectrum for the up-ramp. At block 504 the power spectraldensity (PSD) of the down-ramp beat signal is calculated, and thelocations (frequencies) of the tallest N peaks are located in frequencyspectrum for the down-ramp.

In parallel with free-space interferometer measurements, at blocks 505and 506 reference beat signals are generated from the same laser source.At block 505 a reference beat signal is generated from a reference(fixed-length) interferometer using an upwards frequency chirp(up-ramp). At block 506 a reference beat signal is generated from thereference interferometer using a downwards frequency chirp (down-ramp).At block 507 the phase φ_(ex)(t) of the up-ramp beat signal iscalculated. At block 508 the phase φ_(ex)(t) of the down-ramp beatsignal is calculated.

The N frequency spectrum peaks in the up-ramp PSD and down ramp PSDcorrespond to N different return paths in free-space. By pairing eachpeak in the up-ramp PSD with each peak in the down-ramp PSD correctly,the length of these paths, and the rate at which those path lengths arechanging (i.e., relative velocity of the target) can be calculated. Atblock 509 the first peak in the up-ramp PSD is paired with the firstpeak in the down ramp PSD. This pairing yields an estimate for thetarget distance, velocity, and time-of-flight τ_(est). At block 510time-dependent phase φ_(ex)(t) of the laser up-ramp acquired from thereference interferometer is delayed. The delay applied to phaseφ_(ex)(t) is the duration of estimated time-of-flight Test, whichgenerates delayed phase φ_(ex)(t-τ_(est)). At block 511 delayed phaseφ_(ex)(t-τ_(est)) is subtracted from the undelayed time-dependent phaseφ_(ex)(t) of the laser up-ramp to produce delta phase Δφ(t). Delta phaseΔφ(t) is the estimate of the contribution of phase noise andnonlinearity to the free-space beat signal generated by at block 501. Atblock 512 a conjugate phasor is constructed from delta phase Δφ(t). Atblock 513 the conjugate phasor is multiplied with the free-space up-rampbeat signal to cancel the phase noise and produce a denoised beatsignal. At block 514 the PSD of the denoised beat signal is calculated,and the resulting peaks are located.

Each set of peaks identified at block 509 is evaluated. At block 515peak pairs are checked to determine if more (unevaluated) peak pairsremain in the up and down ramp PSDs. If peak pairs remain, blocks509-515 are repeated for each remaining pair. If all pairs have beentested, block 515 proceeds to block 516. At block 516 the comparisonsare made for peak values of the PSDs calculated for each pair, in orderto determine which pairings were correct (correct pairings occur whenthe PSD peak values are maximized in the up-ramp). At block 517 peakpairings are selected after verification that the pairings are correct.

After the correct peak pairings have been selected in block 517,multi-target identification system 570 may proceed to block 553 to endoperations. Alternatively, after block 517, multi-target identificationsystem 570 may repeat removal of phase noise to improve thesignal-to-noise ratio (SNR) of the down-ramp signals. Using the peakpairs, the distances of all of the measured path lengths are estimated.Based on these path lengths or based on estimated time-of-flight Testfor the path lengths, at block 518 delayed phase φ_(ex)(t-τ_(est)) isproduced by delaying time-dependent phase φ_(ex)(t) estimated for thedown-ramp. At block 519 delayed phase φ_(ex)(t-τ_(est)) is subtractedfrom the undelayed time-dependent phase φ_(ex)(t) of the laser down-rampto produce delta phase Δφ(t). Delta phase Δφ(t) is the estimate of thecontribution of phase noise and nonlinearity to the free-space beatsignal generated by the down-ramp at block 502. At block 520 a conjugatephasor is constructed from delta phase Δφ(t). At block 521 the conjugatephasor is multiplied with the free-space down-ramp beat signal to cancelthe phase noise and produce a denoised beat signal. At block 522 the PSDof the denoised beat signal is calculated, and the resulting peaks arelocated. At block 523 checks are made to determine if peak pairs remain.If so, the blocks 518-523 are repeated. If all peaks have beenprocessed, the final distances and velocities of multiple targets can becomputed, and block 523 proceeds to block 553 to end operations ofmulti-target identification system 570.

FIGS. 6A and 6B illustrate an example of an operation cycle 600 of anFMCW LIDAR that incorporates phase estimation, active phasecancellation, predistortion waveform generation, and peak pairing fit,according to various implementations of the disclosure.

At block 601, the system and laser are powered on. At block 602, thelaser's temperature is allowed to stabilize, using data produced bytemperature sensors 603. Once the laser temperature has stabilized, atblock 605, the system loads an existing laser drive waveform 604, inorder to modulate the laser frequency. Depending on wear on the system,changes in environmental state, etc., laser drive waveform 604 that hasbeen loaded may not be optimal. At block 606, a check may be performedto determine if the frequency characteristics of the laser (e.g., chirprate and chirp nonlinearity) satisfactorily meet specification. If lasercharacteristics are within specification, block 606 proceeds to block609 (shown in FIG. 6B). If the laser characteristics are out ofspecification, block 606 proceeds to block 607. In block 607, in siturefinement of laser drive waveform 604 is performed. The in siturefinement may be performed in accordance with predistortion waveformgenerator 400 and/or predistortion waveform generator 430 (shown inFIGS. 4A and 4B). Block 607 proceeds to both block 608 and block 609(shown in FIG. 6B). At block 608, the updated waveform is saved for thenext power cycle.

Turning to FIG. 6B, if the existing version of laser drive waveform 604meets specification, or alternatively, if the in situ refinement hascompleted, block 609 begins a process of capturing frame data. Block 609may include a number of sub-operations. As the laser frequency ismodulated, at block 610 the LIDAR system waits for a trigger indicatingthat an up-ramp (increasing frequency chirp) has begun. In response, atblock 611 performance of a free-space FMCW LIDAR measurement istriggered, and, in parallel, at block 612 measurement of thetime-dependent laser phase fluctuations (noise and nonlinearity) istriggered. Operations associated with blocks 611 and 612 may correspondto optical phase measurement apparatus 100 (shown in FIG. 1). Theresults of the measurements of block 611 and block 612 are combined atblock 613. Operations at block 613 may represent operations of phasenoise cancellation system 300 (shown in FIG. 3A), phase noisecancellation system 340 (shown in FIG. 3B), multi-target identificationsystem 500 (shown in FIG. 5A), and/or multi-target identification system570 (shown in FIG. 5B). Operations of block 613 may improve the fidelityof the free-space LIDAR measurements. At block 614 the beat signalspectrum is computed. Blocks 610-614 are repeated for a down-ramp. Atblock 615, based on the resulting filtered PSDs, the range and velocityof a point in a scene can be computed.

Typically, a LIDAR frame comprises more than one point. At block 616 theLIDAR system determines if more points remain in the frame. If morepoints remain in the frame, block 616 proceeds to block 617 where theposition of the beam emitted by the FMCW LIDAR system is modified, andthe up/down ramp capture process of blocks 610-615 is repeated. Once allpoints in the frame have been captured, block 616 proceeds to block 618where a point cloud can be assembled, which completes operation cycle600.

The order in which some or all of the process blocks appear in systemsand process 300, 340, 400, 430, 500, 570, and/or 600 should not bedeemed limiting. Rather, one of ordinary skill in the art having thebenefit of the present disclosure will understand that some of theprocess blocks may be executed in a variety of orders not illustrated,or even in parallel.

FIG. 7A illustrates an example autonomous vehicle 700 that may includethe LIDAR designs of FIGS. 1-6, in accordance with aspects of thedisclosure. The illustrated autonomous vehicle 700 includes an array ofsensors configured to capture one or more objects of an externalenvironment of the autonomous vehicle and to generate sensor datarelated to the captured one or more objects for purposes of controllingthe operation of autonomous vehicle 700. FIG. 7A shows sensors 733A,733B, 733C, 733D, and 733E. FIG. 7B illustrates a top view of autonomousvehicle 700 including sensors 733F, 733G, 733H, and 733I in addition tosensors 733A, 733B, 733C, 733D, and 733E. Any of sensors 733A, 733B,733C, 733D, 733E, 733F, 733G, 733H, and/or 733I may include LIDARdevices that include the designs of FIGS. 1-6. FIG. 7C illustrates ablock diagram of an example system 799 for autonomous vehicle 700. Forexample, autonomous vehicle 700 may include powertrain 702 includingprime mover 704 powered by energy source 706 and capable of providingpower to drivetrain 708. Autonomous vehicle 700 may further includecontrol system 710 that includes direction control 712, powertraincontrol 714, and brake control 716. Autonomous vehicle 700 may beimplemented as any number of different vehicles, including vehiclescapable of transporting people and/or cargo and capable of traveling ina variety of different environments. It will be appreciated that theaforementioned components 702-716 can vary widely based upon the type ofvehicle within which these components are utilized.

The implementations discussed hereinafter, for example, will focus on awheeled land vehicle such as a car, van, truck, or bus. In suchimplementations, prime mover 704 may include one or more electric motorsand/or an internal combustion engine (among others). The energy sourcemay include, for example, a fuel system (e.g., providing gasoline,diesel, hydrogen), a battery system, solar panels or other renewableenergy source, and/or a fuel cell system. Drivetrain 708 may includewheels and/or tires along with a transmission and/or any othermechanical drive components suitable for converting the output of primemover 704 into vehicular motion, as well as one or more brakesconfigured to controllably stop or slow the autonomous vehicle 700 anddirection or steering components suitable for controlling the trajectoryof the autonomous vehicle 700 (e.g., a rack and pinion steering linkageenabling one or more wheels of autonomous vehicle 700 to pivot about agenerally vertical axis to vary an angle of the rotational planes of thewheels relative to the longitudinal axis of the vehicle). In someimplementations, combinations of powertrains and energy sources may beused (e.g., in the case of electric/gas hybrid vehicles). In someimplementations, multiple electric motors (e.g., dedicated to individualwheels or axles) may be used as a prime mover.

Direction control 712 may include one or more actuators and/or sensorsfor controlling and receiving feedback from the direction or steeringcomponents to enable the autonomous vehicle 700 to follow a desiredtrajectory. Powertrain control 714 may be configured to control theoutput of powertrain 702, e.g., to control the output power of primemover 704, to control a gear of a transmission in drivetrain 708,thereby controlling a speed and/or direction of the autonomous vehicle700. Brake control 716 may be configured to control one or more brakesthat slow or stop autonomous vehicle 700, e.g., disk or drum brakescoupled to the wheels of the vehicle.

Other vehicle types, including but not limited to off-road vehicles,all-terrain or tracked vehicles, or construction equipment willnecessarily utilize different powertrains, drivetrains, energy sources,direction controls, powertrain controls and brake controls, as will beappreciated by those of ordinary skill having the benefit of the instantdisclosure. Moreover, in some implementations some of the components canbe combined, e.g., where directional control of a vehicle is primarilyhandled by varying an output of one or more prime movers. Therefore,implementations disclosed herein are not limited to the particularapplication of the herein-described techniques in an autonomous wheeledland vehicle.

In the illustrated implementation, autonomous control over autonomousvehicle 700 is implemented in vehicle control system 720, which mayinclude one or more processors in processing logic 722 and one or morememories 724, with processing logic 722 configured to execute programcode (e.g., instructions 726) stored in memory 724. Processing logic 722may include graphics processing unit(s) (GPUs) and/or central processingunit(s) (CPUs), for example. Vehicle control system 720 may beconfigured to control powertrain 702 of autonomous vehicle 700 inresponse to an output of the optical mixer of a LIDAR pixel. Vehiclecontrol system 720 may be configured to control powertrain 702 ofautonomous vehicle 700 in response to outputs from a plurality of LIDARpixels. Vehicle control system 720 may be configured to controlpowertrain 702 of autonomous vehicle 700 in response to outputs frommicrocomputer 203 generated based on signals received from FPA system202.

Sensors 733A-733I may include various sensors suitable for collectingdata from an autonomous vehicle's surrounding environment for use incontrolling the operation of the autonomous vehicle. For example,sensors 733A-733I can include RADAR unit 734, LIDAR unit 736, 3Dpositioning sensor(s) 738, e.g., a satellite navigation system such asGPS, GLONASS, BeiDou, Galileo, or Compass. The LIDAR designs of FIGS.1-6 may be included in LIDAR unit 736. LIDAR unit 736 may include aplurality of LIDAR sensors that are distributed around autonomousvehicle 700, for example. In some implementations, 3D positioningsensor(s) 738 can determine the location of the vehicle on the Earthusing satellite signals. Sensors 733A-733I can optionally include one ormore ultrasonic sensors, one or more cameras 740, and/or an InertialMeasurement Unit (IMU) 742. In some implementations, camera 740 can be amonographic or stereographic camera and can record still and/or videoimages. Camera 740 may include a Complementary Metal-Oxide-Semiconductor(CMOS) image sensor configured to capture images of one or more objectsin an external environment of autonomous vehicle 700. IMU 742 caninclude multiple gyroscopes and accelerometers capable of detectinglinear and rotational motion of autonomous vehicle 700 in threedirections. One or more encoders (not illustrated) such as wheelencoders may be used to monitor the rotation of one or more wheels ofautonomous vehicle 700.

The outputs of sensors 733A-733I may be provided to control subsystems750, including, localization subsystem 752, trajectory subsystem 756,perception subsystem 754, and control system interface 758. Localizationsubsystem 752 is configured to determine the location and orientation(also sometimes referred to as the “pose”) of autonomous vehicle 700within its surrounding environment, and generally within a particulargeographic area. The location of an autonomous vehicle can be comparedwith the location of an additional vehicle in the same environment aspart of generating labeled autonomous vehicle data. Perception subsystem754 may be configured to detect, track, classify, and/or determineobjects within the environment surrounding autonomous vehicle 700.Trajectory subsystem 756 is configured to generate a trajectory forautonomous vehicle 700 over a particular timeframe given a desireddestination as well as the static and moving objects within theenvironment. A machine learning model in accordance with severalimplementations can be utilized in generating a vehicle trajectory.Control system interface 758 is configured to communicate with controlsystem 710 in order to implement the trajectory of the autonomousvehicle 700. In some implementations, a machine learning model can beutilized to control an autonomous vehicle to implement the plannedtrajectory.

It will be appreciated that the collection of components illustrated inFIG. 7C for vehicle control system 720 is merely exemplary in nature.Individual sensors may be omitted in some implementations. In someimplementations, different types of sensors illustrated in FIG. 7C maybe used for redundancy and/or for covering different regions in anenvironment surrounding an autonomous vehicle. In some implementations,different types and/or combinations of control subsystems may be used.Further, while subsystems 752-758 are illustrated as being separate fromprocessing logic 722 and memory 724, it will be appreciated that in someimplementations, some or all of the functionality of subsystems 752-758may be implemented with program code such as instructions 726 residentin memory 724 and executed by processing logic 722, and that thesesubsystems 752-758 may in some instances be implemented using the sameprocessor(s) and/or memory. Subsystems in some implementations may beimplemented at least in part using various dedicated circuit logic,various processors, various field programmable gate arrays (“FPGA”),various application-specific integrated circuits (“ASIC”), various realtime controllers, and the like, as noted above, multiple subsystems mayutilize circuitry, processors, sensors, and/or other components.Further, the various components in vehicle control system 720 may benetworked in various manners.

In some implementations, autonomous vehicle 700 may also include asecondary vehicle control system (not illustrated), which may be used asa redundant or backup control system for autonomous vehicle 700. In someimplementations, the secondary vehicle control system may be capable ofoperating autonomous vehicle 700 in response to a particular event. Thesecondary vehicle control system may only have limited functionality inresponse to the particular event detected in primary vehicle controlsystem 720. In still other implementations, the secondary vehiclecontrol system may be omitted.

In some implementations, different architectures, including variouscombinations of software, hardware, circuit logic, sensors, and networksmay be used to implement the various components illustrated in FIG. 7C.Each processor may be implemented, for example, as a microprocessor andeach memory may represent the random access memory (“RAM”) devicescomprising a main storage, as well as any supplemental levels of memory,e.g., cache memories, non-volatile or backup memories (e.g.,programmable or flash memories), or read-only memories. In addition,each memory may be considered to include memory storage physicallylocated elsewhere in autonomous vehicle 700, e.g., any cache memory in aprocessor, as well as any storage capacity used as a virtual memory,e.g., as stored on a mass storage device or another computer controller.Processing logic 722 illustrated in FIG. 7C, or entirely separateprocessing logic, may be used to implement additional functionality inautonomous vehicle 700 outside of the purposes of autonomous control,e.g., to control entertainment systems, to operate doors, lights, orconvenience features.

In addition, for additional storage, autonomous vehicle 700 may alsoinclude one or more mass storage devices, e.g., a removable disk drive,a hard disk drive, a direct access storage device (“DASD”), an opticaldrive (e.g., a CD drive, a DVD drive), a solid state storage drive(“SSD”), network attached storage, a storage area network, and/or a tapedrive, among others. Furthermore, autonomous vehicle 700 may include auser interface 764 to enable autonomous vehicle 700 to receive a numberof inputs from a passenger and generate outputs for the passenger, e.g.,one or more displays, touchscreens, voice and/or gesture interfaces,buttons and other tactile controls. In some implementations, input fromthe passenger may be received through another computer or electronicdevice, e.g., through an app on a mobile device or through a webinterface.

In some implementations, autonomous vehicle 700 may include one or morenetwork interfaces, e.g., network interface 762, suitable forcommunicating with one or more networks 770 (e.g., a local area network(“LAN”), a wide area network (“WAN”), a wireless network, and/or theInternet, among others) to permit the communication of information withother computers and electronic devices, including, for example, acentral service, such as a cloud service, from which autonomous vehicle700 receives environmental and other data for use in autonomous controlthereof. In some implementations, data collected by one or more sensors733A-733I can be uploaded to computing system 772 through network 770for additional processing. In such implementations, a time stamp can beassociated with each instance of vehicle data prior to uploading.

Processing logic 722 illustrated in FIG. 7C, as well as variousadditional controllers and subsystems disclosed herein, generallyoperates under the control of an operating system and executes orotherwise relies upon various computer software applications,components, programs, objects, modules, or data structures, as may bedescribed in greater detail below. Moreover, various applications,components, programs, objects, or modules may also execute on one ormore processors in another computer coupled to autonomous vehicle 700through network 770, e.g., in a distributed, cloud-based, orclient-server computing environment, whereby the processing required toimplement the functions of a computer program may be allocated tomultiple computers and/or services over a network.

Routines executed to implement the various implementations describedherein, whether implemented as part of an operating system or a specificapplication, component, program, object, module or sequence ofinstructions, or even a subset thereof, will be referred to herein as“program code.” Program code typically comprises one or moreinstructions that are resident at various times in various memory andstorage devices, and that, when read and executed by one or moreprocessors, perform the steps necessary to execute steps or elementsembodying the various aspects of the invention. Moreover, whileimplementations have and hereinafter may be described in the context offully functioning computers and systems, it will be appreciated that thevarious implementations described herein are capable of beingdistributed as a program product in a variety of forms, and thatimplementations can be implemented regardless of the particular type ofcomputer readable media used to actually carry out the distribution.Examples of computer readable media include tangible, non-transitorymedia such as volatile and non-volatile memory devices, floppy and otherremovable disks, solid state drives, hard disk drives, magnetic tape,and optical disks (e.g., CD-ROMs, DVDs) among others.

In addition, various program code described hereinafter may beidentified based upon the application within which it is implemented ina specific implementation. However, it should be appreciated that anyparticular program nomenclature that follows is used merely forconvenience, and thus the invention should not be limited to use solelyin any specific application identified and/or implied by suchnomenclature. Furthermore, given the typically endless number of mannersin which computer programs may be organized into routines, procedures,methods, modules, objects, and the like, as well as the various mannersin which program functionality may be allocated among various softwarelayers that are resident within a typical computer (e.g., operatingsystems, libraries, API's, applications, applets), it should beappreciated that the invention is not limited to the specificorganization and allocation of program functionality described herein.

Those skilled in the art, having the benefit of the present disclosure,will recognize that the exemplary environment illustrated in FIG. 7C isnot intended to limit implementations disclosed herein. Indeed, thoseskilled in the art will recognize that other alternative hardware and/orsoftware environments may be used without departing from the scope ofimplementations disclosed herein.

The term “processing logic” (e.g., processing logic 722) in thisdisclosure may include one or more processors, microprocessors,multi-core processors, Application-specific integrated circuits (ASIC),and/or Field Programmable Gate Arrays (FPGAs) to execute operationsdisclosed herein. In some implementations, memories (not illustrated)are integrated into the processing logic to store instructions toexecute operations and/or store data. Processing logic may also includeanalog or digital circuitry to perform the operations in accordance withimplementations of the disclosure.

A “unit” in this disclosure may be constructed with hardware components(e.g., AND, OR, NOR, XOR gates), may be implemented as circuitryimbedded in one or more processors, ASICs, FPGAs, or photonic integratedcircuits (PIC), and/or may be partially defined as software instructionsstored in one or more memories within a LIDAR system. As an example, thevarious units disclosed herein may be at least partially implemented inLIDAR processing engine 201, microcomputer 203, laser controller 205,and/or FPA driver 209 (shown in FIG. 2), according to implementations ofthe present disclosure.

A “memory” or “memories” described in this disclosure may include one ormore volatile or non-volatile memory architectures. The “memory” or“memories” may be removable and non-removable media implemented in anymethod or technology for storage of information such ascomputer-readable instructions, data structures, program modules, orother data. Example memory technologies may include RAM, ROM, EEPROM,flash memory, CD-ROM, digital versatile disks (DVD), high-definitionmultimedia/data storage disks, or other optical storage, magneticcassettes, magnetic tape, magnetic disk storage or other magneticstorage devices, or any other non-transmission medium that can be usedto store information for access by a computing device.

A Network may include any network or network system such as, but notlimited to, the following: a peer-to-peer network; a Local Area Network(LAN); a Wide Area Network (WAN); a public network, such as theInternet; a private network; a cellular network; a wireless network; awired network; a wireless and wired combination network; and a satellitenetwork.

Communication channels may include or be routed through one or morewired or wireless communication utilizing IEEE 802.11 protocols, SPI(Serial Peripheral Interface), I²C (Inter-Integrated Circuit), USB(Universal Serial Port), CAN (Controller Area Network), cellular dataprotocols (e.g. 3G, 4G, LTE, 5G), optical communication networks,Internet Service Providers (ISPs), a peer-to-peer network, a Local AreaNetwork (LAN), a Wide Area Network (WAN), a public network (e.g. “theInternet”), a private network, a satellite network, or otherwise.

A computing device may include a desktop computer, a laptop computer, atablet, a phablet, a smartphone, a feature phone, a server computer, orotherwise. A server computer may be located remotely in a data center orbe stored locally.

The processes explained above are described in terms of computersoftware and hardware. The techniques described may constitutemachine-executable instructions embodied within a tangible ornon-transitory machine (e.g., computer) readable storage medium, thatwhen executed by a machine will cause the machine to perform theoperations described. Additionally, the processes may be embodied withinhardware, such as an application specific integrated circuit (“ASIC”) orotherwise.

A tangible non-transitory machine-readable storage medium includes anymechanism that provides (i.e., stores) information in a form accessibleby a machine (e.g., a computer, network device, personal digitalassistant, manufacturing tool, any device with a set of one or moreprocessors, etc.). For example, a machine-readable storage mediumincludes recordable/non-recordable media (e.g., read only memory (ROM),random access memory (RAM), magnetic disk storage media, optical storagemedia, flash memory devices, etc.).

The above description of illustrated implementations of the invention,including what is described in the Abstract, is not intended to beexhaustive or to limit the invention to the precise forms disclosed.While specific implementations of, and examples for, the invention aredescribed herein for illustrative purposes, various modifications arepossible within the scope of the invention, as those skilled in therelevant art will recognize.

These modifications can be made to the invention in light of the abovedetailed description. The terms used in the following claims should notbe construed to limit the invention to the specific implementationsdisclosed in the specification. Rather, the scope of the invention is tobe determined entirely by the following claims, which are to beconstrued in accordance with established doctrines of claiminterpretation.

What is claimed is:
 1. A light detection and ranging (LIDAR) systemcomprising: a laser waveform function to define a laser waveform; a setof parameters that at least partially define the laser waveform; and acalibration unit configured to estimate a partial derivative of afrequency response with respect to each parameter in the set ofparameters, wherein the frequency response is measured from an output ofa laser driven by the laser waveform, wherein the calibration unit isconfigured to update the set of parameters to cause the frequencyresponse of the laser to satisfy conditions defined by the laserwaveform function.
 2. The LIDAR system of claim 1 further comprising: afixed-length interferometer, wherein the calibration unit is configuredto receive an in-phase signal and a quadrature signal from thefixed-length interferometer, wherein the calibration unit is configuredto determine a frequency response of the laser based on the in-phasesignal and the quadrature signal.
 3. The LIDAR system of claim 1,wherein the calibration unit is configured to iteratively construct thelaser waveform based the laser waveform function and the set ofparameters, wherein the set of parameters includes an initial version ofthe set of parameters that is superseded by one or more updated versionsof the set of parameters.
 4. The LIDAR system of claim 1, wherein thecalibration unit is configured to iteratively evaluate the frequencyresponse of the laser with updated versions of the set of parameters. 5.The LIDAR system of claim 4, wherein to iteratively evaluate thefrequency response of the laser, the calibration unit is configured toload the laser waveform into a digital to analog converter, wait for thelaser to settle, measure an output from an interferometer, and calculatethe frequency response from the output from the interferometer.
 6. TheLIDAR system of claim 1, wherein the calibration unit is configured toestimate a gradient of the laser waveform function.
 7. The LIDAR systemof claim 6, wherein to estimate the gradient of the laser waveformfunction, the calibration unit is configured to calculate a perturbedversion of the laser waveform, load the perturbed version of the laserwaveform into a digital to analog converter, measure the output from thelaser, and evaluate a perturbed version of the laser waveform function.8. The LIDAR system of claim 7, wherein the perturbed version of thelaser waveform includes a difference between a first parameter in theset of parameters and a second parameter in the set of parameters. 9.The LIDAR system of claim 1, wherein the calibration unit is configuredto update the set of parameters based on the partial derivative of thefrequency response with respect to each parameter in the set ofparameters.
 10. The LIDAR system of claim 1, wherein the calibrationunit is configured to update the set of parameters to compensate fordistortion characteristics of the laser.
 11. An autonomous vehiclecontrol system comprising: a light detection and ranging (LIDAR) systemincluding a laser waveform function to define a laser waveform, a set ofparameters that at least partially define the laser waveform, and acalibration unit configured to estimate a partial derivative of afrequency response with respect to each parameter in the set ofparameters, wherein the frequency response is measured from an output ofa laser driven by the laser waveform, wherein the calibration unit isconfigured to update the set of parameters to cause the frequencyresponse of the laser to satisfy conditions defined by the laserwaveform function; and one or more processors to control the autonomousvehicle control system in response to the laser waveform at leastpartially defined by the calibration unit.
 12. The autonomous vehiclecontrol system of claim 11 further comprising: a fixed-lengthinterferometer, wherein the calibration unit is configured to receive anin-phase signal and a quadrature signal from the fixed-lengthinterferometer, wherein the calibration unit is configured to determinea frequency response of the laser based on the in-phase signal and thequadrature signal.
 13. The LIDAR system of claim 11, wherein thecalibration unit is configured to iteratively construct the laserwaveform based the laser waveform function and the set of parameters,wherein the set of parameters includes an initial version of the set ofparameters that is superseded by one or more updated versions of the setof parameters.
 14. The LIDAR system of claim 11, wherein the calibrationunit is configured to iteratively evaluate the frequency response of thelaser with updated versions of the set of parameters.
 15. The LIDARsystem of claim 14, wherein to iteratively evaluate the frequencyresponse of the laser, the calibration unit is configured to load thelaser waveform into a digital to analog converter, wait for the laser tosettle, measure an output from an interferometer, and calculate thefrequency response from the output from the interferometer.
 16. TheLIDAR system of claim 11, wherein the calibration unit is configured toestimate a gradient of the laser waveform function.
 17. The LIDAR systemof claim 16, wherein to estimate the gradient of the laser waveformfunction, the calibration unit is configured to calculate a perturbedversion of the laser waveform, load the perturbed version of the laserwaveform into a digital to analog converter, measure the output from thelaser, and evaluate a perturbed version of the laser waveform function.18. An autonomous vehicle comprising: a light detection and ranging(LIDAR) system including a laser waveform function to define a laserwaveform, a set of parameters that at least partially define the laserwaveform, and a calibration unit configured to estimate a partialderivative of a frequency response with respect to each parameter in theset of parameters, wherein the frequency response is measured from anoutput of a laser driven by the laser waveform, wherein the calibrationunit is configured to update the set of parameters to cause thefrequency response of the laser to satisfy conditions defined by thelaser waveform function; and one or more processors to control theautonomous vehicle in response to the laser waveform at least partiallydefined by the calibration unit.
 19. The autonomous vehicle of claim 18,wherein the calibration unit is configured to iteratively evaluate thefrequency response of the laser with updated versions of the set ofparameters.
 20. The autonomous vehicle of claim 18, wherein thecalibration unit is configured to estimate a gradient of the laserwaveform function.